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  <div class="section" id="batching">
<span id="guide-batching"></span><h1>Batching<a class="headerlink" href="#batching" title="Permalink to this headline">¶</a></h1>
<p>In practice, we usually need to convert a collection of small graph into a large graph where each original small graph
is a connected component of the large graph. This operation is called <cite>batching</cite> in graph deep learning and is widely
applied to improve computing efficiency.</p>
<p><code class="docutils literal notranslate"><span class="pre">GraphData</span></code> provides interfaces for batching and unbatching graphs for training and inference. The <code class="docutils literal notranslate"><span class="pre">to_batch()</span></code>
function takes a list of <code class="docutils literal notranslate"><span class="pre">GraphData</span></code> instances and returns a single <code class="docutils literal notranslate"><span class="pre">GraphData</span></code> which is the merged large graph.
On the other hand, users may use <code class="docutils literal notranslate"><span class="pre">from_batch()</span></code> to decompose a large graph generated by merging small graphs into a
list of <code class="docutils literal notranslate"><span class="pre">GraphData</span></code>.</p>
<p>The following code snippet shows an example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">g_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">batched_edges</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">graph_edges_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># Build a number of graphs</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
    <span class="n">g</span> <span class="o">=</span> <span class="n">GraphData</span><span class="p">()</span>
    <span class="n">g</span><span class="o">.</span><span class="n">add_nodes</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
        <span class="n">g</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">src</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">tgt</span><span class="o">=</span><span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="mi">10</span><span class="p">)</span>
        <span class="n">batched_edges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">+</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">+</span> <span class="p">((</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="mi">10</span><span class="p">)))</span>
    <span class="n">g</span><span class="o">.</span><span class="n">node_features</span><span class="p">[</span><span class="s1">&#39;idx&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">i</span>
    <span class="n">g</span><span class="o">.</span><span class="n">edge_features</span><span class="p">[</span><span class="s1">&#39;idx&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">i</span>
    <span class="n">graph_edges_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">get_all_edges</span><span class="p">())</span>
    <span class="n">g_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>

<span class="c1"># Test to_batch</span>
<span class="n">batch</span> <span class="o">=</span> <span class="n">to_batch</span><span class="p">(</span><span class="n">g_list</span><span class="p">)</span>

<span class="n">target_batch_idx</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
        <span class="n">target_batch_idx</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="c1"># Expected behaviors</span>
<span class="k">assert</span> <span class="n">batch</span><span class="o">.</span><span class="n">batch</span> <span class="o">==</span> <span class="n">target_batch_idx</span>
<span class="k">assert</span> <span class="n">batch</span><span class="o">.</span><span class="n">get_node_num</span><span class="p">()</span> <span class="o">==</span> <span class="mi">50</span>
<span class="k">assert</span> <span class="n">batch</span><span class="o">.</span><span class="n">get_all_edges</span><span class="p">()</span> <span class="o">==</span> <span class="n">batched_edges</span>

<span class="c1"># Un-batching</span>
<span class="n">graph_list</span> <span class="o">=</span> <span class="n">from_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">graph_list</span><span class="p">)):</span>
    <span class="n">g</span> <span class="o">=</span> <span class="n">graph_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="c1"># Expected behaviors</span>
    <span class="k">assert</span> <span class="n">g</span><span class="o">.</span><span class="n">get_all_edges</span><span class="p">()</span> <span class="o">==</span> <span class="n">graph_edges_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="k">assert</span> <span class="n">g</span><span class="o">.</span><span class="n">get_node_num</span><span class="p">()</span> <span class="o">==</span> <span class="mi">10</span>
    <span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">node_features</span><span class="p">[</span><span class="s1">&#39;idx&#39;</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">i</span><span class="p">))</span>
    <span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">edge_features</span><span class="p">[</span><span class="s1">&#39;idx&#39;</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">i</span><span class="p">))</span>
</pre></div>
</div>
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